Colors of an image captured using an imaging apparatus are generated based on light from environmental illumination light during imaging reflected off a surface of a subject and impinging upon the imaging apparatus. Accordingly, colors of a captured image vary with different environmental illumination light even when a subject having the same surface reflectance is imaged. In techniques of recognizing an object in an image using a hyperspectral image in which light of a large number of wavelengths is recorded, it is undesirable that a captured spectrum vary with a change of environmental illumination light in spite of the fact that the subject is the same object. To solve the problem, it is necessary to estimate an optical spectrum of environmental illumination light and correct an effect thereof.
A method of correcting an effect of variation of an optical spectrum of environmental illumination light has been already proposed, which involves estimating a color temperature of environmental illumination light for a captured color image represented by three channels of RGB, and correcting the image from the estimated color temperature (see PTLs 1 and 2).
In PTL 1, for example, color information for gray and/or skin color contained in an image is used to estimate a color temperature of environmental illumination light, and a rate of correction for converting the temperature to a target color temperature is calculated for each channel to correct the image.
In PTL 2, the color temperature that minimizes an evaluation value calculated from a spectral energy distribution of blackbody radiation and an eigenvector of a subject is estimated as the color temperature of an imaging light source. To apply these techniques to a hyperspectral image, a rate of correction for each wavelength may be calculated using an optical spectrum of environmental illumination light generated from the estimated color temperature and an optical spectrum in the target environment. Specifically, representing an optical spectrum intensity of a certain pixel in an input captured image at a wavelength λ as L(λ), an estimated illumination spectrum intensity during imaging as I(λ), and a target illumination spectrum intensity as I′(λ), an optical spectrum intensity L′(λ) for that pixel in an output image can be calculated as given by EQ. (1):
                    [                  EQ          .                                          ⁢          1                ]                                                                                  L            ′                    ⁡                      (            λ            )                          =                                                            I                ′                            ⁡                              (                λ                )                                                    I              ⁡                              (                λ                )                                              ·                      L            ⁡                          (              λ              )                                                          (                  EQ          .                                          ⁢          1                )            
Although these methods effectively work for color images, they pose a problem that correction for hyperspectral images is insufficient because there is a large error between the optical spectrum generated from the color temperature and that of actual environmental illumination light.
When the imaging environment is limited to the outdoors, an optical spectrum of environmental illumination light that is main there is a spectrum of insolation from sunlight observed on the ground surface. A method of estimating a spectrum of insolation under clear skies with high accuracy is proposed in NPL 1.
The method of estimating an optical spectrum in NPL 1 estimates attenuation or scatter of sunrays in the atmosphere and incidence thereof to the subject surface based on insolation conditions during imaging calculated from the date and time and place information, such as the solar zenith angle, atmospheric turbidity or precipitable water, and information on tilt of the subject surface to calculate a direct component Id, which is insolation equivalent to so-called direct sunrays onto the subject surface, and a scatter component Is, which represents environmental light, and calculates their sum as an insolation spectrum I. Accordingly, this method can estimate environmental illumination light in the outdoors under clear skies with high accuracy.
A diagram of a configuration of the image processing method in accordance with the techniques in the related art described above is shown in FIG. 12. The diagram of the configuration in FIG. 12 is a block diagram generated based on PTLs 1 and 2, and NPL 1. Clear-sky illumination spectrum calculating means 12 receives an input of insolation conditions during imaging, and uses the method of calculation according to NPL 1 to calculate an illumination spectrum during imaging under clear skies.
Illumination spectrum correcting means 11 receives inputs of an input image captured outdoors, the illumination spectrum during imaging estimated by the clear-sky illumination spectrum calculating means 12, and a target illumination spectrum stored in a target illumination spectrum storage memory 13, and uses the method according to EQ. (1) to convert the input image into an image as captured under the target illumination spectrum.